Solutions Architect for growth-stage and enterprise orgs.Now shipping production AI systems with Claude.
I’m Shirley. Pre-sales Solutions Architecture, and post-sales Customer Success Engineering and Support leadership at Clearbit (acq. HubSpot) and Demandbase— serving tier-one customers like Stripe, Slack, Meta, Intercom, and Segment. Today, in Mexico City: five live businesses and AI-native systems I ship with Claude daily.
After two years out, this is the role.
The honest version
First, the obvious: after stepping away from tech, I've been busy building and supporting five businesses in Mexico City. It's gone surprisingly well. The last couple of years have been about engineering myself out of the day-to-day — building the org, the processes, and the platform (RIOS) so the businesses run without me as a single point of failure. My partner now leads the full operation and the day-to-day, with a fully capable team behind her; the businesses continue whether I'm involved or not. Stepping back from operations was the goal. Anthropic was attractive to me a year ago too — the timing just wasn't right then. It's starting to feel right now.
What pulled me toward Anthropic is twofold. This role in particular reads as a distinct match for the work I've actually done — pre-sales Solutions Architecture and post-sales Customer Success Engineering ownership for growth-stage and enterprise customers, founder-led technical conversations, and the architectural advisory work that lives between technical evaluation and production deployment. And Anthropic itself is, by almost any measure, one of the most attractive places in tech right now — the work, the team, and the trajectory all line up. I've been using Claude as a daily operator across my businesses, and the rate of capability gains release-over-release has been hard to miss — it's rare to watch a product redefine what's possible this quickly. The honest pull is the learning curve. Being inside the work at Anthropic — instead of watching it from the outside — is the better seat. I want to be at Anthropic. This role is the cleanest fit; if the team thinks I'd land harder somewhere adjacent, I'd hear that conversation too.
After a decade of doing this, my honest evaluation criteria for any job have stayed the same three things, in this order: a place with an overwhelming amount of opportunities to continue learning, a smart team, and cool products. That framework has never led me wrong. Anthropic clears all three.
Joined Clearbit pre-Series A and built Solutions Architecture, Customer Success Engineering, and Support Engineering as net-new functions, leading them through the company’s growth phase — supporting tier-one accounts including Stripe, Slack, Meta, Intercom, and Segment. (Acquired by HubSpot in 2023.) Demandbase before that, leading SE for Fortune 500 customers across the same vertical mix Industries serves. Recently shipped three production AI systems with Claude. The combination I’d bring on day one: a decade building and leading pre-sales customer engineering, plus hands-on Claude experience.
I’m applying to lead Solutions Architecture, Applied AI (Industries) at Anthropic.— here’s how my experience maps to it.
Four core responsibilities from the JD, mapped to the closest evidence in my actual experience.
- JD line 1
Hire, manage, and guide a team of pre-sales Solutions Architects by providing both technical guidance and career development.
My evidenceBuilding and leading customer engineering teams from the ground up has been the most rewarding part of my career. At Clearbit, I established Solutions Architecture, Customer Success Engineering, and Support Engineering as net-new functions spanning four product surfaces: Salesforce package, APIs, native integrations, and the platform itself. I hired and developed engineers and architects, raised the bar for technical guidance, and created career paths that supported growth from Support roles through Customer Success Engineering and Engineering project leaders. Years of running that, through Series A growth and a HubSpot acquisition, is the closest analog to building this team at Anthropic.
- JD line 2
Act as a technical sponsor for high-value strategic customers and advise them on their overall AI adoption strategies or use case scoping and POC execution.
My evidenceTechnical sponsorship for strategic customers has been the work — owning architecture conversations from initial scoping through POC through deployment, and earning the credibility that drove renewal and expansion at Clearbit and Demandbase. The recent Claude work adds practitioner grounding to those conversations: I’ve shipped enough to speak from experience, not theory.
- JD line 3
Partner closely with Industries sales leadership to identify new strategies to drive adoption of Anthropic products within specific verticals or horizontal use cases.
My evidencePartnership with sales leadership has been daily work for a decade. At Demandbase I led pre-sales for ABM solutions across financial services, technology, healthcare, and retail customers — the same vertical mix Anthropic Industries serves. At Clearbit, owning both pre-sales and post-sales technical work meant operating in step with sales leadership on every strategic deal: sourcing patterns, prioritizing target accounts, building the technical case that moved deals from evaluation to close. The reps trusted me in the room because what my teams and I committed to in pre-sales shipped without surprises post-close.
- JD line 4
Work with cross-functional teams like product and engineering to ensure Anthropic prioritizes customer feedback or resolves blockers to adoption.
My evidenceClosing the customer-to-product feedback loop has been the core of every customer engineering function I’ve built. At Clearbit I stood up Solutions Architecture, Customer Success Engineering, and Support with explicit feedback pathways — success metrics, customer development, and escalation patterns all feeding product strategy. Across years of running that function, the patterns we surfaced from 1,000+ customer engagements drove what shipped next.
Three builds in production.
Measurement and reporting infrastructure across five operating businesses — completeness, reconciliation, and the executive layer that makes the numbers actionable.
riosuno.comA normalized product catalog, eligibility schema, and programmatic surface — the platform-shaped work of comparing 104 mortgage products across a standardized set of attributes.
casaruta.comA capital matching layer for Mexican SMBs. Rule-based today; the AI underwriting layer comes later, sitting on top of the rules that already work — not in place of them.
capitalruta.comOperational intelligence across a multi-vertical portfolio of operating businesses
An executive dashboard and data-completeness layer that unifies POS, inventory, and accounting signal across five operating businesses.
Each business in the portfolio runs on its own combination of POS, inventory, accounting, and staffing tools. Reports disagreed by 5–15% on any given day. The decisions that mattered — staffing tomorrow, ordering next week, whether last week’s pricing change worked — were being made on lagged, incomplete, or inconsistent data.
- 01Ingestion
Vendor adapters (POS and PDF/CSV imports) backfill historical windows on a rolling cursor and watch for late-arriving rows. Every ingestion run is logged and safe to re-run — running the same job twice produces the same result, never duplicate data.
- 02Completeness
A first-class completeness layer per entity (Company, Brand, Location) — every dashboard view declares what data it depends on and refuses to render misleading numbers when something is missing or stale.
- 03Reconciliation
Compares POS, accounting, and bank deposits in the same units so the numbers actually line up. When they don't, the system flags the gap instead of quietly averaging it away.
- 04Executive layer
A small, opinionated set of executive views — daily revenue and trend, ticket size, item mix, labor against forecast, supplier variance — built around what an operator actually decides on, not what's easy to chart.
- 05Agent layer
Claude on top, scoped to an explicit memory of business structure, vendors, and known anomalies — used for explanation, summarization, and drafting weekly operating notes that are reviewed before they leave the system.
Used. Claude handles the unstructured inputs that feed the database — receipts and statements — extracting structured fields so the deterministic side can reconcile against them. Every output is validated before it lands.
- Month-end close happens in real time instead of after a three-week wait — books are continuous, not retrospective.
- We cut waste 5% in the first month, driven by real-time margin and cost alerting.
- AI extracts invoices (CFDIs, PDFs, XMLs) and matches them to bank deposits and POS sales — manual reconciliation is gone. For us, that’s two full-time jobs eliminated.
- Payroll and labor-cost-as-percent-of-revenue tracked continuously per location, not at the end of the period.
- Multi-entity, multi-brand, multi-location P&L in one engine — plug a new business in and the executive layer is online in days.
Ten plus years building, integrating, and deploying the tools sales, marketing, and revenue teams rely on.
Before AI.
Click any row to expand
So, what’s the easiest way to become a founder?
- Pick a country where your vocabulary is roughly “hola, gracias, cenicero.”
- Pick a tax code where your coffee comes with an XML.
- Hire one person. Find out ‘at-will employment’ doesn’t translate.
- Pick anything but the one thing you’ve done for ten plus years. Do it again anyway.
After a decade in tech, I’d done most of what I’d set out to do — except build a business from zero, on my own terms. So my partner and I moved to CDMX and together started a small portfolio of operating businesses. The systems thinking carried over. Only the inputs changed — customers, staff, regulators, supply chain.
Five live locations across CDMX, all operating today. The startup skillsets I built in tech — instrumentation, measurement, systems thinking, ruthless prioritization — are the direct reason we’ve been able to scale from one location to five, and what sets us apart from most operators in the category.
- 01Early on — work at a big company.
- 02Build and lead great teams.
- 03Make meaningful contributions to a company that lead to a successful exit event.
- 04Start my own thing and make it successful.
- Analytics and Attribution
- ICP and Targeting
- Forecasting and Modeling
- Modern Martech Familiarity
Now: Manager of SolutionsArchitecture, Anthropic.
If the JD lands — let’s talk
A decade architecting and solutioning across growth-stage and enterprise tech. Three years out, building businesses and shipping production AI on Claude. The role here matches what I know; Anthropic is the place I’d come back for.
The resume’s below. The cover letter is this page. The rest is whatever you need to ask me.